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Area of Science:

  • Computational physics
  • Chemical engineering
  • Materials science

Background:

  • Smoothed dissipative particle dynamics (SDPD) is a particle-based simulation method.
  • SDPD offers thermodynamic consistency and control over fluid transport properties.
  • Modeling advection-diffusion-reaction (ADR) dynamics in complex systems is challenging.

Purpose of the Study:

  • To develop an SDPD model that incorporates sub-particle scale reactant transport and compositional field evolution.
  • To enable the simulation of complex systems governed by ADR dynamics.
  • To validate the model's effectiveness in diverse simulation scenarios.

Main Methods:

  • Implementation of the novel SDPD model within the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS).
  • Validation through benchmark problems covering diffusion-dominated, reaction-dominated, and coupled ADR regimes.
  • Analysis of simulation results for accuracy and predictive capability.

Main Results:

  • The SDPD model successfully simulates systems with sub-particle scale reactant transport.
  • The model accurately captures various ADR dynamics, including diffusion and reaction limitations.
  • Demonstrated ability to reproduce complex phenomena like Turing pattern formation.

Conclusions:

  • The developed SDPD model provides a robust framework for simulating ADR dynamics.
  • This methodology is suitable for mesoscopic and macroscopic modeling of soft matter systems.
  • The model has broad applicability in biology, chemistry, materials science, and environmental engineering.